
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowerCamelCase_    :			Optional[Any]    =	logging.get_logger(__name__)

class 							_UpperCamelCase					(       _A  ,							_A	):






			'''simple docstring'''

			__UpperCamelCase   :    int          =			"""maskformer-swin"""

			__UpperCamelCase   :    Optional[Any]          =			{
			    """num_attention_heads""": """num_heads""",
			    """num_hidden_layers""": """num_layers""",
			}

			def __init__(							self			:							Optional[int]    ,						snake_case_			:							int=224    ,						snake_case_			:							Optional[int]=4    ,						snake_case_			:							List[Any]=3    ,						snake_case_			:							str=96    ,						snake_case_			:							Any=[2, 2, 6, 2]    ,						snake_case_			:							Dict=[3, 6, 12, 24]    ,						snake_case_			:							List[str]=7    ,						snake_case_			:							Any=4.0    ,						snake_case_			:							Dict=True    ,						snake_case_			:							List[str]=0.0    ,						snake_case_			:							Dict=0.0    ,						snake_case_			:							List[str]=0.1    ,						snake_case_			:							Any="gelu"    ,						snake_case_			:							Tuple=False    ,						snake_case_			:							str=0.02    ,						snake_case_			:							Optional[int]=1e-5    ,						snake_case_			:							int=None    ,						snake_case_			:							Any=None    ,						**snake_case_			:							List[Any]    ,						):
							super().__init__(**snake_case_   )

							UpperCamelCase_:				Optional[Any]						=    image_size
							UpperCamelCase_:				Optional[int]						=    patch_size
							UpperCamelCase_:				Optional[int]						=    num_channels
							UpperCamelCase_:				Optional[int]						=    embed_dim
							UpperCamelCase_:				Any						=    depths
							UpperCamelCase_:				List[str]						=    len(snake_case_   )
							UpperCamelCase_:				str						=    num_heads
							UpperCamelCase_:				Any						=    window_size
							UpperCamelCase_:				List[str]						=    mlp_ratio
							UpperCamelCase_:				Dict						=    qkv_bias
							UpperCamelCase_:				Any						=    hidden_dropout_prob
							UpperCamelCase_:				List[str]						=    attention_probs_dropout_prob
							UpperCamelCase_:				Any						=    drop_path_rate
							UpperCamelCase_:				int						=    hidden_act
							UpperCamelCase_:				Dict						=    use_absolute_embeddings
							UpperCamelCase_:				List[Any]						=    layer_norm_eps
							UpperCamelCase_:				Dict						=    initializer_range
							# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
							# this indicates the channel dimension after the last stage of the model
							UpperCamelCase_:				int						=    int(embed_dim * 2 ** (len(snake_case_   ) - 1)   )
							UpperCamelCase_:				Any						=    ["""stem"""] + [f'''stage{idx}''' for idx in range(1    ,						len(snake_case_   ) + 1   )]
							UpperCamelCase_, UpperCamelCase_:				Tuple						=    get_aligned_output_features_output_indices(
							    out_features=snake_case_    ,						out_indices=snake_case_    ,						stage_names=self.stage_names   )








from __future__ import annotations







def 							A__       (    lowerCamelCase    )	->						int:
				UpperCamelCase_:				Optional[Any]						=    len(lowerCamelCase    ) // 2

				# choose the middle 3 elements
				UpperCamelCase_:				Tuple						=    lst[m - 1 : m + 2]

				# if middle element is peak
				if three[1] > three[0] and three[1] > three[2]:
								return three[1]

				# if increasing, recurse on right
				elif three[0] < three[2]:
								if len(lst[:m]    ) == 2:
												m -= 1
								return peak(lst[m:]    )

				# decreasing
				else:
								if len(lst[:m]    ) == 2:
												m += 1
								return peak(lst[:m]    )


if __name__ == "__main__":
							import doctest

							doctest.testmod()






